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Type: Conference paper
Title: Optimising the mutual information of ecological data clusters using genetic algorithms
Author: Maier, H.
Radbone, L.
Finkemeyer, T.
Hume, T.
Butchart, M.
Goonan, P.
Citation: MODSIM 2003 : International Congress on Modelling and Simulation, Jupiters Hotel and Casino, 14-17 July 2003 : integrative modelling of biophysical, social and economic systems for resource management solutions : proceedings / David A. Post (ed.): pp.807-812
Publisher: The Modelling and Simulation Soc of Aust and NZ Inc
Publisher Place: IAS, ANU, Canberra
Issue Date: 2003
ISBN: 174052098X
Conference Name: International Congress on Modelling and Simulation (15th : 2003 : Townsville, Queensland)
Editor: Post, D.
Statement of
Holger R. Maier, Lucy Radbone, Tom Finkemeyer, Tiana Hume, Miranda Butchart and Peter Goonan
Abstract: The Australian River Assessment System (AusRivAS) is a nation-wide program designed to assess the health of Australian rivers and streams. The general AusRivAS method involves the establishment of a database of reference sites, which are sites that are considered to be minimally affected by anthropogenic impacts. These sites are then grouped into clusters of similar macroinvertebrate communities. The clusters are analysed to find relationships between the physical, geographical and chemical properties of sites in a cluster and the corresponding macroinvertebrate communities. The relationships found are then used to predict the macroinvertebrate communities at non-reference sites that would be expected if these sites were equivalent to least disturbed reference conditions. To determine the level of river health, the expected macroinvertebrate community is compared with the observed community. As part of AusRivAS, the clustering step is conducted using the statistical Unweighted Pair Group Arithmetic Averaging (UPGMA) method. A potential shortcoming of this approach is that it uses a linear performance measure for grouping similar data points. A recently developed approach for clustering ecological data (MIR-max) overcomes this limitation by using mutual information as the performance measure. In this paper, an alternative to the MIRmax technique (MIRA4) is proposed, which uses genetic algorithms for optimising the overall mutual information of the ecological data clusters. The MIR-max and MIRA4 approaches are applied to the South Australian combined season riffle AusRivAS data, and the results obtained are compared with those obtained using the UPGMA method. The results indicate that the overall mutual information values of the clusters obtained using MIR-max and MIRA4 are significantly higher than those obtained using the UPGMA method, and that the use of genetic algorithms is successful in determining clusters with higher overall mutual information values compared with those obtained using MIR-max for the case study considered.
Keywords: AusRivAS
River health assessment
Genetic algorithm
Mutual information
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Appears in Collections:Aurora harvest 6
Civil and Environmental Engineering publications
Environment Institute publications

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